Triple
T5134133
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Donald Sterling |
E115775
|
entity |
| Predicate | banType |
P62811
|
FINISHED |
| Object | lifetime ban |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: lifetime ban | Statement: [Donald Sterling, banType, lifetime ban]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: banType Context triple: [Donald Sterling, banType, lifetime ban]
-
A.
banYear
Indicates the year in which a ban was enacted or came into effect on the related entity or activity.
-
B.
canBlock
Indicates that one entity has the ability or permission to prevent, obstruct, or stop the action or effect of another entity.
-
C.
bannedBy
Indicates that an entity is prohibited or disallowed as a result of a decision or action taken by another entity.
-
D.
fareType
Indicates the category or class of fare (such as standard, discounted, or promotional) that applies to a given trip, ticket, or pricing instance.
-
E.
patronType
Indicates the classification or category of a patron in relation to a service, institution, or resource.
- F. None of above. chosen
Provenance (4 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69bd444426bc819099ccd23f141e22aa |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd7fef2e8c8190982dd67f50295ada |
completed | March 20, 2026, 5:12 p.m. |
| PD | Predicate disambiguation | batch_69bd77ac2fc48190abeebb003a82384c |
completed | March 20, 2026, 4:37 p.m. |
| PDg | Predicate description generation | batch_69bd7fee42748190967013828973cce0 |
completed | March 20, 2026, 5:12 p.m. |
Created at: March 20, 2026, 1:43 p.m.